remove enbale hccl

example/resnet101_imagenet/eval.py

@@ -51,17 +51,11 @@ context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True, parameter_broadcast=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True, parameter_broadcast=True)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        init()
 
     epoch_size = config.epoch_size
     net = resnet101(class_num=config.class_num)

example/resnet101_imagenet/train.py

@@ -56,17 +56,11 @@ context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True, parameter_broadcast=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True, parameter_broadcast=True)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        init()
 
     epoch_size = config.epoch_size
     net = resnet101(class_num=config.class_num)

example/resnet50_cifar10/eval.py

@@ -51,17 +51,11 @@ context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        init()
 
     epoch_size = config.epoch_size
     net = resnet50(class_num=config.class_num)

example/resnet50_cifar10/train.py

@@ -54,17 +54,11 @@ context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
-                                              mirror_mean=True)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
+                                          mirror_mean=True)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        init()
 
     epoch_size = config.epoch_size
     net = resnet50(class_num=config.class_num)

example/vgg16_cifar10/eval.py

@@ -37,7 +37,7 @@ if __name__ == '__main__':
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
     context.set_context(device_id=args_opt.device_id)
-    context.set_context(enable_mem_reuse=True, enable_hccl=False)
+    context.set_context(enable_mem_reuse=True)
 
     net = vgg16(num_classes=cfg.num_classes)
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,

example/vgg16_cifar10/train.py

@@ -66,7 +66,7 @@ if __name__ == '__main__':
     context.set_context(device_id=args_opt.device_id)
     context.set_context(enable_task_sink=True)
     context.set_context(enable_loop_sink=True)
-    context.set_context(enable_mem_reuse=True, enable_hccl=False)
+    context.set_context(enable_mem_reuse=True)
 
     device_num = int(os.environ.get("DEVICE_NUM", 1))
     if device_num > 1:

example/yolov3_coco2017/train.py

@@ -90,13 +90,11 @@ if __name__ == '__main__':
     if args_opt.distribute:
         device_num = args_opt.device_num
         context.reset_auto_parallel_context()
-        context.set_context(enable_hccl=True)
         context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True,
                                           device_num=device_num)
         init()
         rank = args_opt.device_id % device_num
     else:
-        context.set_context(enable_hccl=False)
         rank = 0
         device_num = 1
 

mindspore/ccsrc/pipeline/init.cc

@@ -115,8 +115,6 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("set_device_id", &mindspore::MsContext::set_device_id, "Set device id.")
     .def("open_tsd", &mindspore::MsContext::OpenTsd, "Open tdt dataset client.")
     .def("close_tsd", &mindspore::MsContext::CloseTsd, "Close tdt dataset client.")
-    .def("set_hccl_flag", &mindspore::MsContext::set_enable_hccl, "Set enable hccl.")
-    .def("get_hccl_flag", &mindspore::MsContext::enable_hccl, "Get whether to enable hccl.")
     .def("set_task_sink_flag", &mindspore::MsContext::set_enable_task_sink, "Set enable task sink.")
     .def("get_task_sink_flag", &mindspore::MsContext::enable_task_sink, "Get whether to enable task sink.")
     .def("get_save_graphs_flag", &mindspore::MsContext::save_graphs_flag, "Get whether to save graphs.")

mindspore/ccsrc/pipeline/pipeline.cc

@@ -773,7 +773,7 @@ void InitHccl() {
   (void)ms_context->OpenTsd();
   uint32_t device_id = ms_context->device_id();
   std::string device_name = ms_context->device_target();
-
+  ms_context->set_enable_hccl(true);
   if (ms_context->backend_policy() == "ms" && ms_context->device_target() == kAscendDevice) {
     auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(device_name, device_id);
     MS_EXCEPTION_IF_NULL(runtime_instance);

mindspore/context.py

@@ -225,14 +225,6 @@ class _Context:
         if not success:
             raise RuntimeError("Device id set failed!!!")
 
-    @property
-    def enable_hccl(self):
-        return self._context_handle.get_hccl_flag()
-
-    @enable_hccl.setter
-    def enable_hccl(self, hccl):
-        self._context_handle.set_hccl_flag(hccl)
-
     @property
     def enable_ir_fusion(self):
         return self._context_handle.get_ir_fusion_flag()
@@ -482,7 +474,7 @@ def reset_auto_parallel_context():
 
 
 @args_type_check(mode=int, precompile_only=bool, device_target=str,
-                 device_id=int, enable_ir_fusion=bool, save_graphs=bool, enable_hccl=bool,
+                 device_id=int, enable_ir_fusion=bool, save_graphs=bool,
                  enable_task_sink=bool, save_graphs_path=str, enable_loop_sink=bool,
                  enable_mem_reuse=bool, save_ms_model=bool, save_ms_model_path=str, enable_gpu_summary=bool,
                  enable_auto_mixed_precision=bool, enable_dump=bool, save_dump_path=str,
@@ -515,7 +507,6 @@ def set_context(**kwargs):
                     while device_num_per_host should no more than 4096. Default: 0.
         enable_ir_fusion (bool): Whether to enable ir fusion. Default: True.
         save_graphs (bool): Whether to save graphs. Default: False.
-        enable_hccl (bool): Whether to enable hccl. Default: False.
         enable_loop_sink (bool): Whether to enable loop sink. Default: True.
         enable_task_sink (bool): Whether to enable task sink. Default: True.
         enable_mem_reuse (bool): Whether to enable memory reuse. Default: True.

mindspore/nn/wrap/grad_reducer.py

@@ -130,7 +130,7 @@ class DistributedGradReducer(Cell):
         >>>
         >>> device_id = int(os.environ["DEVICE_ID"])
         >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True,
-        >>>                     device_id=int(device_id), enable_hccl=True)
+        >>>                     device_id=int(device_id))
         >>> init()
         >>> context.reset_auto_parallel_context()
         >>> context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL)

tests/st/auto_parallel/onehot_model_parallel.py

@@ -33,7 +33,6 @@ def setup_module():
     global rank_id
     np.random.seed(0)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    context.set_context(enable_hccl=True)
     context.set_context(enable_task_sink=True,
                         device_id=device_id)
     context.set_context(enable_ir_fusion=True)

tests/st/auto_parallel/soft_entropy_loss_expand_parallel.py

@@ -46,7 +46,6 @@ def setup_module():
     global rank_id
     np.random.seed(0)
     context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    context.set_context(enable_hccl=True)
     context.set_context(enable_task_sink=True,
                         device_id=device_id)
     context.set_context(enable_ir_fusion=True)

tests/st/auto_parallel/test_resnet50_expand_loss_2p.py

@@ -31,7 +31,6 @@ from mindspore.train.callback import Callback
 from mindspore.parallel import set_algo_parameters
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-context.set_context(enable_hccl=True)
 context.set_context(enable_task_sink=True, device_id=int(os.getenv('DEVICE_ID')))
 context.set_context(enable_ir_fusion=True)
 context.set_context(enable_loop_sink=False)

tests/st/mem_reuse/resnet_cifar_memreuse.py

@@ -122,16 +122,10 @@ class CrossEntropyLoss(nn.Cell):
 
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
-            context.set_auto_parallel_context(all_reduce_fusion_split_indices=[140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
+        context.set_auto_parallel_context(all_reduce_fusion_split_indices=[140])
+        init()
 
     context.set_context(mode=context.GRAPH_MODE)
     epoch_size = args_opt.epoch_size

tests/st/mem_reuse/resnet_cifar_normal.py

@@ -123,16 +123,10 @@ class CrossEntropyLoss(nn.Cell):
 
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
-            context.set_auto_parallel_context(all_reduce_fusion_split_indices=[140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
+        context.set_auto_parallel_context(all_reduce_fusion_split_indices=[140])
+        init()
 
     context.set_context(mode=context.GRAPH_MODE)
     epoch_size = args_opt.epoch_size

tests/st/tbe_networks/resnet_cifar.py

@@ -122,16 +122,10 @@ class CrossEntropyLoss(nn.Cell):
 
 
 if __name__ == '__main__':
-    if args_opt.do_eval:
-        context.set_context(enable_hccl=False)
-    else:
-        if args_opt.run_distribute:
-            context.set_context(enable_hccl=True)
-            context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
-            auto_parallel_context().set_all_reduce_fusion_split_indices([140])
-            init()
-        else:
-            context.set_context(enable_hccl=False)
+    if not args_opt.do_eval and args_opt.run_distribute:
+        context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL)
+        auto_parallel_context().set_all_reduce_fusion_split_indices([140])
+        init()
 
     context.set_context(mode=context.GRAPH_MODE)
     epoch_size = args_opt.epoch_size

tests/st/tbe_networks/test_resnet_cifar_8p.py

@@ -153,7 +153,6 @@ def train_process(q, device_id, epoch_size, num_classes, device_num, batch_size,
     context.set_context(enable_task_sink=True, device_id=device_id)
     context.set_context(enable_loop_sink=True)
     context.set_context(enable_mem_reuse=True)
-    context.set_context(enable_hccl=enable_hccl)
     os.environ['MINDSPORE_HCCL_CONFIG_PATH'] = MINDSPORE_HCCL_CONFIG_PATH
     os.environ['RANK_ID'] = str(device_id)
     os.environ['RANK_SIZE'] = str(device_num)

tests/ut/python/parallel/test_auto_parallel_resnet.py

@@ -19,6 +19,7 @@ from mindspore import Tensor
 from mindspore.ops import operations as P
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.common.initializer import TruncatedNormal
+from mindspore.communication.management import init
 from mindspore.train.model import Model, ParallelMode
 from mindspore import context
 import os
@@ -31,10 +32,10 @@ from mindspore.parallel import set_algo_parameters
 from mindspore.parallel import _cost_model_context as cost_model_context
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-context.set_context(enable_hccl=True)
 context.set_context(enable_task_sink=True, device_id= 0)
 context.set_context(enable_ir_fusion=True)
 context.set_context(enable_loop_sink=False)
+init()
 
 def weight_variable(shape, factor=0.1):
     return TruncatedNormal(0.02)